The Dynamic and Endogenous Behavior of Re-Offense Risk: An Agent-Based Simulation Study of Treatment Allocation in Incarceration Diversion Programs

Incarceration-diversion treatment programs aim to improve societal reintegration and reduce recidivism, but limited capacity forces policymakers to make prioritization decisions that often rely on risk assessment tools. While predictive, these tools typically treat risk as a static, individual attribute, which overlooks how risk evolves over time and how treatment decisions shape outcomes through social interactions. In this paper, we develop a new framework that models reoffending risk as a human-system interaction, linking individual behavior with system-level dynamics and endogenous community feedback. Using an agent-based simulation calibrated to U.S. probation data, we evaluate treatment allocation policies under different capacity constraints and incarceration settings. Our results show that no single prioritization policy dominates. Instead, policy effectiveness depends on temporal windows and system parameters: prioritizing low-risk individuals performs better when long-term trajectories matter, while prioritizing high-risk individuals becomes more effective in the short term or when incarceration leads to shorter monitoring periods. These findings highlight the need to evaluate risk-based decision systems as sociotechnical systems with long-term accountability, rather than as isolated predictive tools.